ArticleJournal of intensive care2026
Exploratory characterization of dynamic soluble programmed death-ligand 1 trajectories and their association with mortality in critical coronavirus disease 2019.
Article in Journal of intensive care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundPersistent immune checkpoint activation is a recognized feature of critical coronavirus disease 2019 (COVID-19). However, the temporal behavior and clinical utility of soluble programmed death-ligand 1 (sPD-L1) remain unclear. We investigated the longitudinal changes in sPD-L1, its relationship with organ dysfunction markers, and their prognostic value when combined with machine learning (ML) models.
methodsIn this single-center observational study, we included 40 adults with severe COVID-19 pneumonia admitted to the intensive care unit (ICU) (April 2021-December 2022), and 23 healthy volunteers as controls. We measured plasma sPD-L1 on ICU day 1, 5, 7, 14, and 21. Routine biochemistry, complete blood counts, and arterial blood gas analyses were conducted in parallel. Cox regression was used to identify independent predictors of hospital mortality, the primary outcome. Eight ML classifiers were trained using admission variables and sPD-L1 levels from ICU day 1, 5, and 7. Discrimination was assessed using stratified fivefold cross-validation, and feature importance was evaluated using Shapley Additive Explanations (SHAP).
resultsOf 40 patients, 10 died during hospitalization. Overall, sPD-L1 levels declined during the ICU stay but remained persistently high in non-survivors. ICU day 5 and 7 values differed significantly between survivors and non-survivors (p = 0.023 and 0.001, respectively). In multivariable Cox analysis, ICU day 7 sPD-L1 levels and arterial lactate levels on admission independently predicted mortality. ICU day 7 sPD-L1 levels correlated positively with creatinine, C-reactive protein, and fibrinogen levels (all p < 0.05) in cross-sectional correlation analyses. Among ML models, the support vector machine achieved the highest discriminative accuracy (mean area under the curve = 0.917). ICU day 5 sPD-L1 was designated as the primary predictor of mortality based on SHAP analysis, with lactate contributing minimally.
conclusionSustained sPD-L1 elevation during the first ICU week is strongly associated with early organ dysfunction and independently predicts death in critical COVID-19. Incorporating serial sPD-L1 measurements into bedside ML models significantly enhances risk discrimination. These findings support sPD-L1 as an integrative biomarker of the immune-renal-coagulation interplay, warranting validation in larger multicenter cohorts and exploration as a potential companion marker for immune-modulatory interventions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.